From Location to Valuation: Analysing Real Estate with Places Insights in BigQuery
- 28East
- Sep 19, 2025
- 2 min read
How do you determine property value? For many real estate professionals, this is an important question that goes beyond just looking at a street address.
Being able to quantify how key location characteristics impact property value can help businesses provide better forecasting for real estate prices, while giving agents and developers key insights into valuation trends.
This is where Google’s Places Insights can become a game-changer for businesses looking to give their real estate analysis a competitive edge. Let’s dive into what that means in practice.
The Challenge of Quantifying a Neighbourhood
Without quantifiable methods to help you understand property valuation, pricing new properties can fall to expert judgement and knowledge of the local market. This makes it hard to scale and ensure accuracy without bias.
Here’s how you can move to a more data-driven approach.
Use Places Insights to identify factors that impact property value
The solution is a streamlined workflow that combines Google Cloud’s scale with the rich location data of Google Maps Platform.
Places Insights, available via Analytics Hub, offers an analysis-ready dataset of points of interest (POIs), covering over 300 place types and attributes like accessibility, parking, and payment options.
Start with a property dataset (e.g. location and price), then use Places Insights to count nearby POIs, such as restaurants or transit stations, within a defined radius.
For example, in a Navagis case study, they analysed amenities within 500 metres of each property. The result is a new, improved dataset with additional columns showing counts of relevant POIs, allowing you to see how location amenities may influence property value.
Once your property data is enriched, you can use BigQuery ML to predict property values.
With just a few lines of SQL, you can train a model on your own pricing data to see how nearby amenities affect property prices. The model runs inside BigQuery, so there’s no need to move data or set up complex systems either!
What This Means for Businesses
These insights can be used across industries to improve their data and analysis.
Real estate investors can assess neighbourhoods based on nearby amenities and test how future developments might affect value.
Realtors can highlight features that matter most to local buyers. Urban planners can decide where to add parks, transport, or public services for the biggest impact.
And retailers can use the data for smarter site selection, choosing areas with the right mix of amenities to attract their ideal customers.
Build Smarter with Machine Learning and 28East
When you combine your own business data with Google Maps Platform’s rich location context, you go beyond basic mapping to get access to smarter insights that drive better decision-making.
At 28East, we help African businesses make location data work harder. If you're ready to explore what’s possible with geospatial analytics, get in touch to see how we can help.
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